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Convequity on X

$RBRK's backup engine breaks data into fixed-size blocks, for example 4 MB, and stores only the unique blocks across backups. Commvault and Cohesity instead divide data into variable-sized chunks, with the boundaries set by the content itself. Each design has a trade-off. With fixed blocks, a small change inside a block means the whole block gets written again, so Rubrik uses more storage. In return, hashing and comparing blocks is simple, which means faster backups and lower compute overhead. With variable chunks, only the changed portions are written, so storage use is much lower. Working out chunk boundaries and hashes as the data comes in takes more CPU and memory, which can slowdown backups. Cohesity offsets part of this with a scale-out architecture that processes in parallel across nodes. The two designs fit two different jobs. Variable-length deduplication suits long-term archives, where storage cost matters more than speed. Fixed-block deduplication suits recovery after a hack or ransomware attack, where fast restores and predictable performance matter more than saving storage. That matches how each company started. Rubrik came into the market in 2014 focused on security and on getting critical data back quickly after a breach. Cohesity started from a different idea. It built its own file system that spreads data across many servers, each with its own computing power. Backup copies stay readable like normal files, so companies could search and analyze them where they sit, without restoring them first. Security came second. The two are now moving closer together. Cohesity has leaned much more into security and recovery, especially after buying Veritas's backup business in 2024.
@convequity

Convequity on X

$NTSK began as a CASB vendor, governing how employees use SaaS applications at the point of use. Years of that work gave it application-specific knowledge of user behavior, data movement, and risk inside cloud apps. That knowledge carries over to AI. Netskope inspects prompts, responses, and file transfers inline and in real time, and it can respond with something more precise than a block: preventing a risky copy-and-paste, stepping up authentication for a sensitive interaction, or coaching the user as they work. The part that matters more comes after the prompt. AI adds autonomy and chaining, so a single human interaction can trigger many machine-executed actions across tools, data stores, and services, often outside one user session. If data is over-permissioned and tools are loosely scoped, an agent can stay inside its allowed permissions and still cause harm faster than a person can intervene. The context Netskope gathers at the interaction can be carried forward to constrain that downstream execution: who the user is, what data is involved, which action was taken, and how risky it looks. So Netskope's edge is that it already knows the user, the data and the action at the moment of the prompt, and it can use that context to limit what an agent does next. It is now building the agent side directly. Its Agentic Broker rates agent actions by risk before they run, and its AI posture management for agents built on AWS, Azure and GCP is still in preview. That is the part to watch.

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